[MRG] ENH validate sample_weight with _check_sample_weight in IsotonicRegression #16203
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rth
reviewed
Jan 27, 2020
| sample_weight = np.ones(len(y), dtype=y.dtype) | ||
| else: | ||
| sample_weight = np.array(sample_weight[order], dtype=y.dtype) | ||
| sample_weight = _check_sample_weight(sample_weight, y, dtype=y.dtype) |
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Thanks!
You need to apply the order parameter, maybe as,
if sample_weight is None:
sample_weight = np.ones(len(y), dtype=y.dtype)
else:
sample_weight = _check_sample_weight(sample_weight, y, dtype=y.dtype)
sample_weight = sample_weight[order]There was a problem hiding this comment.
don't you think
sample_weight = _check_sample_weight(sample_weight, y, dtype=y.dtype)
sample_weight = sample_weight[order]
is enough ? It makes the code simpler and reordering ones has no effect and efficiency is not a problem there.
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It makes the code simpler and reordering ones has no effect and efficiency is not a problem there.
If you prefer. Slicing an array with int indexing does have some cost, but it should indeed be minimal.
|
It has been done in #16322. Sorry @marijavlajic for the confusion but someone did the same thing and his PR got merged first. We still need to be better in coordination :/ |
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Reference Issues/PRs
Fixes #15358 for IsotonicRegression.
What does this implement/fix? Explain your changes.
Replaces custom validation logic with standardized method utils.validation._check_sample_weight.
Any other comments?
Worked with @gelavizh1 @lschwetlick at scikit-learn sprint.
Worked also on the same issue in naive_bayes.py / for BaseDiscreteNB but it looks like the appropriate changes have already been made.
CC @adrinjalali @noatamir #WiMLDS